Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

Abstract

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1-90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400-2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

“…Additionally, for the adulteration identification of Tartary buckwheat from Sichuan and Shanxi, the R 2 p of the model constructed by SNV-DT was higher than that of other preprocessing methods, and the RMSEP was also smaller. This finding was due to the ability of SNV-DT to effectively eliminate the drift of the spectral curve caused by the distance difference between the optical fiber probe and the sample, consistent with the experimental results of the previous application of preprocessing methods to improve the model's prediction effect ( Yi et al, 2017 ; Bala et al, 2022 ).…”

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

“…To date, many advanced analytical techniques have been proposed and used for grain quality analysis, of which spectroscopy and computer vision are the most common non-invasive techniques. Spectroscopic techniques, including near infrared reflectance spectroscopy (NIRS), have been widely applied in the agricultural field to replace the time-consuming conventional analytical methods (9)(10)(11). The technique is based on the differential absorption of nearinfrared wavelengths by molecules containing -C-H, -C-O-H, and -C-N-H bonds, which are the major NIR bands in biological materials.…”

Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics

“…The application of NIRS in context of chick pea flour mainly includes determination of chemical constituents of chick pea flour, 27 detection of neutral and acid detergent fiber 28 . We have recently reported detection of maize flour adulteration in chickpea flour using NIRS 29 . As the development and validation of screening method for individual parameter and commodity is a prerequisite, 30 we therefore, for the first time, report NIRS methods for the detection of two legume flours, namely grass pea and pea, adulterants in chickpea flour.…”

“…28 We have recently reported detection of maize flour adulteration in chickpea flour using NIRS. 29 As the development and validation of screening method for individual parameter and commodity is a prerequisite, 30 we therefore, for the first time, report NIRS methods for the detection of two legume flours, namely grass pea and pea, adulterants in chickpea flour. Spectral data of pure chickpea flour, and adulterated chickpea with grass pea and pea flour were processed after pretreatments to develop the best fit calibration models.…”